Filtered likelihood for point processes

Giesecke, Kay; Schwenkler, Gustavo

doi:10.1016/j.jeconom.2017.11.011

Cited by 20 publications

(9 citation statements)

References 55 publications

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“…The filter equations we introduce here are suitable for these purposes, and we devote this appendix to exposing their essential properties. This extremely convenient formalism has, to our knowledge, not been thoroughly exploited, though we note their resemblance to the constructions of Ogata (1978), Puri & Tuan (1986), Kliemann et al (1990), and Giesecke & Schwenkler (2018).…”

Section: B2 Seirs Modelmentioning

confidence: 99%

Markov genealogy processes

King

Lin

Ionides

2022

Theoretical Population Biology

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Section: B2 Seirs Modelmentioning

confidence: 99%

Markov genealogy processes

King

Lin

Ionides

2022

Theoretical Population Biology

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“…Recently, Giesecke and Schwenkler (2018) have discussed marked point processes with applications in finance and economics to model the timing of defaults, corporate bankruptcies, market transactions, unemployment spells, births, and mortgage delinquencies. They developed likelihood estimators for the parameters of a marked point process and incompletely observed explanatory factors that influence the arrival intensity and mark distribution, although they presumed only finite dimensional marks.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Infinitely Stochastic Micro Forecasting

Maciak¹,

Okhrin²,

Pešta³

2019

Preprint

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Forecasting costs is now a front burner in empirical economics. We propose an unconventional tool for stochastic prediction of future expenses based on the individual (micro) developments of recorded events. Consider a firm, enterprise, institution, or state, which possesses knowledge about particular historical events. For each event, there is a series of several related subevents: payments or losses spread over time, which all leads to an infinitely stochastic process at the end. Nevertheless, the issue is that some already occurred events do not have to be necessarily reported. The aim lies in forecasting future subevent flows coming from already reported, occurred but not reported, and yet not occurred events. Our methodology is illustrated on quantitative risk assessment, however, it can be applied to other areas such as startups, epidemics, war damages, advertising and commercials, digital payments, or drug prescription as manifested in the paper. As a theoretical contribution, inference for infinitely stochastic processes is developed. In particular, a non-homogeneous Poisson process with non-homogeneous Poisson processes as marks is used, which includes for instance the Cox process as a special case.

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“…Elliott et al [15] used a stochastic EM technique to solve the filtering equations and provide asymptotic estimates of the filtering control equations. Giesecke et al [16] suggested a method for estimating the default intensity based on filtered likelihood estimation and obtained an analytical solution for parameter estimation. The filtered likelihood technique outperforms other methods for solving the filtered equations in terms of parameter estimation accuracy and asymptotic efficiency in numerical simulations.…”

Section: Introductionmentioning

confidence: 99%

Research on Financial Default Model with Stochastic Intensity Using Filtered Likelihood Method

Liu

2023

Mathematics

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This paper investigates the financial default model with stochastic intensity by incomplete data. On the strength of the process-designated point process, the likelihood function of the model in the parameter estimation can be decomposed into the factor likelihood term and event likelihood term. The event likelihood term can be successfully estimated by the filtered likelihood method, and the factor likelihood term can be calculated in a standardized manner. The empirical study reveals that, under the filtered likelihood method, the first model outperforms the other in terms of parameter estimation efficiency, convergence speed, and estimation accuracy, and has a better prediction effect on the default data in China’s financial market, which can also be extended to other countries, which is of great significance in the default risk control of financial institutions.

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Filtered likelihood for point processes

Cited by 20 publications

References 55 publications

Markov genealogy processes

Markov genealogy processes

Infinitely Stochastic Micro Forecasting

Research on Financial Default Model with Stochastic Intensity Using Filtered Likelihood Method

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